Abstract:

Feature extraction and pattern classification are two key problems in face recognition. In order to solve the high-dimensional and Small Sample Size (SSS) problem of face recognition, start with the feature extraction of human face and dimensionality reduction algorithms, a quadratic feature extraction and dimensionality reduction algorithm model was put forward based on Restricted Boltzmann Machine (RBM). At first, the image was evenly divided into a number of local image blocks and quantified, then the image was processed by Gabor wavelet transformation. The Gabor facial features were encoded by RBM to learn more intrinsic characteristics of data, so as to achieve the purpose of dimensionality reduction of high-dimensional facial features. On the basis of that, a multimodal face recognition algorithm based on Deep Belief Network (DBN) was proposed. The recognition results on ORL, UMIST and FERET face databases with different sample sizes and different resolution images show that, compared with the linear dimension reduction method and shallow network method, the proposed method achieves better learning efficiency and good recognition result.

摘要：

特征提取与模式分类是人脸识别的两个关键问题。针对人脸识别中的高维和小样本问题，从人脸特征的提取与降维算法入手，提出基于受限玻尔兹曼机(RBM)的二次特征提取及降维算法模型。首先把图像均匀分成若干局部图像块并进行量化，再对图像进行Gabor小波变换，通过RBM对得到的Gabor人脸特征进行编码，学习数据更本质的特征，从而达到对高维人脸特征降维的目的；并以此为基础提出基于深度信念网络(DBN)的多通道人脸识别算法。在ORL、UMIST和FERET人脸库上对不同样本规模和不同分辨率的图像进行实验，识别结果表明，与采用线性降维和浅层网络的方法相比，所提方法取得了较好的学习效率和很好的识别效果。